Informatics Tools for High-throughput Analysis of Cancer Mutations

用于癌症突变高通量分析的信息学工具

• 批准号: 8606625
• 负责人: Rachel Karchin
• 金额: $ 29万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-17 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8606625
• 关键词: Address; Binding Sites; Bioinformatics; Biological; Cancer Center; Cancer Prognosis; Categories; Classification; Collaborations; Communities; Computer Analysis; Computer software; Country; Data; Databases; Development; Diagnostic; Disease; Doctor of Medicine; Ensure; Gene Frequency; Gene Mutation; Genes; Genetic; Genetic Polymorphism; Genomics; Heterogeneity; Histocompatibility Testing; Housing; Image; Imagery; Informatics; Internet; Letters; Machine Learning; Malignant Neoplasms; Maps; Methods; Missense Mutation; Molecular; Mutagenesis; Mutate; Mutation; Mutation Analysis; Occupations; Pathway Analysis; Pattern; Pharmaceutical Preparations; Population; Positioning Attribute; Privacy; Production; Proteins; Publications; Qualifying; RNA Splicing; Research Infrastructure; Research Personnel; Resources; Roentgen Rays; Scientist; Secure; Site; Source; Structure; Technology; The Cancer Genome Atlas; Transcript; Translations; Tumor Tissue; Untranslated Regions; Update; Variant; Work; anticancer research; base; cancer cell; cancer classification; cancer genome; cancer genomics; cancer therapy; cohort; data exchange; design; exome; exome sequencing; experience; high throughput analysis; improved; insertion/deletion mutation; insight; interest; microbial alkaline proteinase inhibitor; next generation sequencing; novel strategies; prognostic; protein function; protein structure; public health relevance; software development; tool; tumor; user-friendly; web interface; web services

PROJECT SUMMARY Large tumor exome sequencing projects have identified a very large number of mutations whose cancer relevance is not yet understood. To begin to address this need, our team has produced two web applications for high-throughput computational analysis of cancer mutations: the Cancer-Related Analysis of VAriants Toolkit (CRAVAT) and the Mutation Position Imaging Toolbox (MuPIT). CRAVAT accepts millions of mutations in a single batch upload and maps mutations from genomic coordinates to annotated transcripts and proteins. MuPIT currently accepts batch uploads of up to 2500 SNVs and maps from genomic coordinates onto X-ray crystal structures of proteins from Protein Data Bank (PDB). We propose to combine and harden CRAVAT and MuPIT into a single web application, in which we will substantially improve the tools, user interface, software infrastructure, integration with external data resources and tools used by the community, and support for protected data. The scope of all tools in the web application will be broadened to handle analysis of the full range of small-scale mutation consequence types found in cancer exomes. CRAVAT analysis identifies mutations most likely to have deleterious impact on protein function and those that are most likely to confer a selective advantage to cancer cells (drivers), using classifiers developed by our team. Classifier scores are supplemented with annotations, including population allele frequencies, previous occurrence in tumor tissue types, and gene functional categories, enabling filtering (e.g. removing polymorphisms) and prioritization. Gene-level annotation and scoring, by aggregation of classifier scores from mutations in a cohort is also provided. MuPIT maps mutations from genomic positions onto to protein structures and provides interactive viewing of mutations in the context of protein structure, and in relation to a variety of annotations. To enable prioritization of interesting mutations and genes, the application provides a preview describing each structure and all available annotations (e.g., binding sites, experimental mutagenesis results, polymorphic and disease- associated variants that have been previously documented). After selecting a PDB of interest, the user is led to an interactive visualization page. An enhanced Jmol applet displays all SNVs mapped onto the structure. Frequently, many SNVs in the input list can be mapped onto a single structure, revealing clustering patterns around key functional sites. Based only on word-of-mouth, since the debut of the two applications in August 2012, CRAVAT has been utilized by 129 unique users from 39 countries, and it has analyzed 1,136 submitted jobs, totaling 27.9 million mutations. MuPIT has been utilized by 242 unique users from 25 countries, with 720 submitted jobs. (Source: Google Analytics).

项目摘要 大型肿瘤外显子组测序项目已经确定了非常大量的突变，其癌症 相关性尚未得到理解。为了开始满足这一需求，我们的团队制作了两个Web应用程序 用于癌症突变的高通量计算分析：癌症相关变异分析 工具包（CRAVAT）和突变位置成像工具包（MuPIT）。CRAVAT接受数百万种突变 在一个单一的批量上传和映射突变从基因组坐标到注释的转录本和蛋白质。 MuPIT目前接受多达2500个SNV的批量上传，并将基因组坐标映射到X射线上 蛋白质数据库（Protein Data Bank，PDB）我们建议联合收割机和硬化CRAVAT 和MuPIT整合到一个Web应用程序中，我们将大幅改进工具，用户界面， 软件基础设施，与外部数据资源和社区使用的工具的集成，以及支持 保护数据。网络应用程序中所有工具的范围将扩大到处理对整个 在癌症外显子组中发现的一系列小规模突变后果类型。 CRAVAT分析鉴定了最有可能对蛋白质功能产生有害影响的突变， 最有可能赋予癌细胞选择性优势（驱动程序），使用我们开发的分类器， 团队分类器评分补充了注释，包括群体等位基因频率、以前的 肿瘤组织类型和基因功能类别中的发生率，使得能够过滤（例如，去除 多态性）和优先化。基因水平的注释和评分，通过聚集来自 还提供了群组中的突变。 MuPIT将突变从基因组位置映射到蛋白质结构，并提供交互式查看 在蛋白质结构的背景下，以及与各种注释相关的突变。使 优先考虑感兴趣的突变和基因，该应用程序提供了一个预览描述每个结构 以及所有可用的注释（例如，结合位点，实验诱变结果，多态性和疾病- 先前已记录的相关变体）。在选择感兴趣的PDB之后，用户被引导到 交互式可视化页面。一个增强的Jmol applet显示映射到结构上的所有SNV。 通常，输入列表中的许多SNV可以映射到单个结构上，从而揭示聚类模式 围绕关键功能点。 仅凭口碑，自2012年8月两款应用首次亮相以来，CRAVAT一直在 来自39个国家的129个独立用户使用了该软件，它分析了1,136个提交的工作，总计2790万个 突变。MuPIT已被来自25个国家的242个独立用户使用，提交了720个职位。（资料来源： Google Analytics）。